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研究生:許馨尹
研究生(外文):Hsin-Yin Hsu
論文名稱:以類神經模糊網路發展適性化車內導航系統之研究
論文名稱(外文):Developing Adaptive In-vehicle Navigation System based on Fuzzy-Neural Network
指導教授:周碩彥周碩彥引用關係
指導教授(外文):Shuo-Yan Chou
學位類別:碩士
校院名稱:國立臺灣科技大學
系所名稱:工業管理系
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:英文
論文頁數:57
中文關鍵詞:路徑導航系統路徑選擇類神經模糊系統
外文關鍵詞:Route Navigation SystemRoute Choice CriteriaFuzzy-Neural NetworkFuzzy Inference SystemArtificial Neural NetworkANFIS
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Nowadays, the technology on Advanced Traveler Information Systems (ATIS) becomes more and more progressive. This well-developed technology not only keeps travelers from getting lost, but greatly decreases the time people spend on searching the routes to destination. Navigation system provides drivers some choices on selecting routes, for example, shortest time, shortest distance, use of freeways, etc. However, the path which system provides is not always the “optimal” one, since drivers may consider other factors such as familiarity of the route, traffic condition, weather condition, personal preference, and so on. Therefore, the importance of self-learning ability in systems becomes essential and significant.
Due to these facts, this research is mainly about building an adaptive driving route guidance system. In other words, our objective is to add human preference into current navigation system. In this study, we will use Fuzzy-Neural Network (FNN) to format this system. FNN takes advantage of two major techniques, fuzzy and neural network. Fuzzy logic provides a way to model vague attributes by transforming variables between crisp and linguistic values. And from neural network, we can easily make the system learn by itself. At the end, the system will continuously modify and improve the model to generate better results corresponding to the user’s inclination. In this way, the system will be able to act like driver’s thinking logic.
Nowadays, the technology on Advanced Traveler Information Systems (ATIS) becomes more and more progressive. This well-developed technology not only keeps travelers from getting lost, but greatly decreases the time people spend on searching the routes to destination. Navigation system provides drivers some choices on selecting routes, for example, shortest time, shortest distance, use of freeways, etc. However, the path which system provides is not always the “optimal” one, since drivers may consider other factors such as familiarity of the route, traffic condition, weather condition, personal preference, and so on. Therefore, the importance of self-learning ability in systems becomes essential and significant.
Due to these facts, this research is mainly about building an adaptive driving route guidance system. In other words, our objective is to add human preference into current navigation system. In this study, we will use Fuzzy-Neural Network (FNN) to format this system. FNN takes advantage of two major techniques, fuzzy and neural network. Fuzzy logic provides a way to model vague attributes by transforming variables between crisp and linguistic values. And from neural network, we can easily make the system learn by itself. At the end, the system will continuously modify and improve the model to generate better results corresponding to the user’s inclination. In this way, the system will be able to act like driver’s thinking logic.
Abstract II
Acknowledgement III
Table of Contents IV
List of Figures VI
List of Table VII
Chapter 1. Introduction 1
1.1. Motivation and Background 1
1.2. Objective 3
1.3. Methodology 4
1.4. Organization of Thesis 5
Chapter 2. Literature Review 6
2.1. Advanced Traveler Information Systems 6
2.2. Route Selection Criteria 8
2.3. Fuzzy-Neural Networks (FNN) 10
2.4. Adaptive Neural-Fuzzy Inference System (ANFIS) 12
Chapter 3. Model Formulation 16
3.1. System Structure 16
3.2. Model Assumptions and Algorithm 18
3.3. Attributes Settings 24
3.4. Decision-Making System 28
3.5. Self-learning System 33
Chapter 4. Model Implementation 35
4.1. Model Assumptions 35
4.2. Example 36
4.3. Result 46
Chapter 5. Conclusion and Future Research 48
5.1. Conclusion 48
5.2. Future Research 49
Reference 50
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